Multi-Environment and Multi-Year Bayesian Analysis Approach in <i>Coffee canephora</i>
André Monzoli Covre,
Flavia Alves da Silva,
Gleison Oliosi,
Caio Cezar Guedes Correa,
Alexandre Pio Viana,
Fabio Luiz Partelli
Affiliations
André Monzoli Covre
Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
Flavia Alves da Silva
Laboratório de Melhoramento Genético Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Campos dos Goytacazes 28013-602, RJ, Brazil
Gleison Oliosi
Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
Caio Cezar Guedes Correa
Departamento de Agronomia, Universidade Federal do Espírito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
Alexandre Pio Viana
Laboratório de Melhoramento Genético Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Campos dos Goytacazes 28013-602, RJ, Brazil
Fabio Luiz Partelli
Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.
